Data Science Process: Resolve Business Problems Smartly

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Data Science Process: Resolve Business Problems Smartly

Whether you run an empire or a small enterprise, every business faces certain common problems and hitches. The questions like, How do I improve my sales? What is my organization lacking? What to do when old customers are not purchasing my product? What ought to be done with a specific end goal to pull in more clients?

And much more such, often boggle a business owner’s mind. If this is the case with you as well, then there is definitely a path that can offer you a cutting edge over the others and it doesn’t take much effort in guessing what it is. Yes, I am talking about “data science” something that is taking over the corporate world with its capability to make complex things simple.

To understand the applications in a better way, you may look at the working model of big tycoons like Amazon, Uber, Netflix, Starbucks, etc. They all utilize Big data analytics to refine their marketing, manage their finances, predict frauds, evaluate the viewing habits of millions of clients, etc.

This is how Uber is able to provide the easy-to-book cab service and Starbucks does not suffer losses even after having three shops at the same location. Isn’t it mesmerizing what all data science can do?

The next question coming to your mind would be how to incorporate the data science process into your particular business module. Or how is this prediction even possible?

Do not worry, all your queries will be answered. But before moving ahead we must understand the basic concept of data science and the fields it merges together.

A data scientist combines concepts of, statistics, analytics, data processing, machine learning, predictive analysis, basic mathematics, and computer science, to bring out the desired outcome that can benefit your business.

Do you run a business or work with the management closely? Have you ever thought about why sales numbers are going down? Is your operational cost going up? Why is it becoming harder day by day to retain clients? Why are customers flocking for competitors’ products? – In case you’ve ever been bothered with any of these types of questions, then you do understand the importance of data.

With the technological revolution, the operational aspect of businesses has changed drastically. Data is the new gold. He who knows how to churn out insights and intel will beat all the competition. Data Science is the methodology for retrieving valuable information from the stockpile of data.

What happens when you decide to incorporate the data science process into your business?

Let’s decipher for you what good can come of making data science practice in your everyday business running. List out some pointers that would probably help you change your mind and walk the path with this premium service enriching your business.

Increase in The Number Of Customers

The transactional & customer engagement data taken at various points from the customers such as the feedback on products, services, etc. helps the data scientists to predict the return on investment for the company.

Also, this data helps the company design products according to the demand pattern of the customers, hence improving the customer base. The marketing analytics designed with the use of predictive analysis is further able to attract valuable clients.

Better Customer Service

The customer’s demographical data is paired with the product he/she buys and is recorded for future reference. This type of data helps understand the type of customers a business must look forward to attracting.

Also, when the customers are presented with other products, their interest in any particular product is recorded, so that demand can be anticipated. Recommendation engines can help in up-selling other products to increase business revenue & recommendations also works as customer delight.

Improved Efficiency

Apart from improving sales and customer demands, the company itself needs to function properly. It necessitates ensuring that all the equipment installed in its facilities is working efficiently.

The industries that deal with perishable goods need to make sure that they do not have extra stock in their warehouses. Data Science can help business owners in inventory optimization. The idea here is to predict the problem before it actually appears so that it can be avoided by hampering the efficient working of a company.

Advantages of using data science in business

Data science holds the key to overhauling businesses for higher efficiency. From understanding the hidden patterns to unlocking the intel, it is the next revolution for industries. Each industry has already been touched by data science and analytics and more and more business owners are seeing the benefits of relevant information for decision-making. A few of the advantages businesses can leverage from this stream are – 

  • Business Predictability
  • Real-time monitoring and intelligence
  • Removes the barriers in Marketing & Sales
  • Understanding of complex dataset
  • Facilitation of key business decision-making.

How can data science be used to solve business problems?

Data is the collection of all the mysterious questions that companies must deal with day in and day out. From declining sales figures to rising costs to HR issues, data holds the key to all the answers. It’s just that you need to know where to look and what to look for. It solves real-time issues with the cocktail of statistics and computer science to churn out hidden insights. It goes deep into the unstructured pile of raw data and gets meaningful intel that can drive business decisions.

1. Upgrades and Improvements

Innovation is the key to surviving and sustaining in the business world. But understanding the pulse of the customers is even more important to know which upgrades and improvements to the existing products/services will be accepted by them and which will be pushed aside.

It’s been the biggest secret that organizations are trying to hack every day. A complete understanding of consumer behavior is nearly impossible, but data science can shed light on this matter with a great level of accuracy. The right set of improvements done as per customers’ feedback can make the product/ service widely acceptable in the market within a short span.

2. New Product or Service Development

Having an idea is nothing till it gets executed properly. Even if the idea gets executed, it needs to match the customers’ expectations and should be able to solve their pain points. A great example would be the streaming company Netflix, which started as an alternative to movie renting but has exponentially grown to become an integral part of every household. From movies to series to games, it dovetails into the vast pile of data on customers’ sentiment and behavior and brings the best insights out of it. Every successful product/service company does the same. 

3. Security enhancement

Those days are long gone when manual security used to suffice the protection measures. Machine learning algorithms merged with AI can enhance security parameters to global standards. From fraud detection to data scanning, it can be leveraged widely in security use cases. Multiple companies are going through the vast set of data for finding patterns and ensuring no security issue is left unchecked. The more the cases are backed by data, the easier it is for the security personnel to verify, validate and fix it. 

4. Future market trend prediction

Identifying the next market trend can be the pivotal key to bringing a revolution in business. What customers would need in the future, being able to predict that, is nothing less than a superpower. Companies that stay ahead of the curve, have always been leveraging the data to understand and predict the next big thing in their respective industry. For example, a Nielsen study found out that a whopping 81% of customers want companies to take environmental sustainability seriously. Clothing retailer Patagonia considered that and launched a worn-wear site, which helped their customers to recycle used products, and in return, brand loyalty improved drastically.

The Data Analytics’ Ways

Data science matters because it enables businesses to strategize and operate more effectively. It is all about adding quality to the performance with the help of data. Predictive analysis is one of the most important aspects of data science that allows companies to make the right decisions. So now, let us dig in deeper to understand the way in which it is carried out

Step 1 – Identify The Business Problem

In order to benefit from data science, one must be clear about the end result that one wants from it.  An effective data strategy can only be formed when the problem is crystal clear.

If there is any ambiguity in defining the problem, the end product will not be satisfactory. For instance, if you wish to improve your sales then only the data related to customers will be extracted and worked upon.

Step 2 – Create Objectives

Once the problem has been identified, the next goal is to define objectives. To do so relevant data needs to be collected from authentic resources.

Once you have the data in your hand, the next step would be to decide what to measure and how to measure it. These answers help to define the flow in which the problem will be solved.

Step 3 – Preparing Data

For the objectives to be fulfilled in the desired timeline, the bulk data needs to process into a useable and easy-to-extract form. For this, data scientists organize the data in a certain format.

All the redundant data is removed and all the missing values are filled. Once the data becomes arranged it is utilized to carry out the process.

Step 4 – Developing Model and Testing

The word model refers to a computer program that works on the data that is being fed to it and produces the business insights we have been looking for. The insights generated are also tested against another data set to check for their efficient working and capability to resolve the issue.

Step 5 – Monitoring and Reiteration

Once the insights have been generated their applications are continuously monitored by the data scientists. This allows the professionals to ensure that the target problem is being properly resolved by the end product. In case any issue arises, the data can be reframed to devise another useful insight.

What Are the Types of Business Analytics?

Business analysis as the name suggests can be defined as the systematic process by which businesses can gain intel for accurate and timely decision-making with the help of statistical models and technology. It can be segregated into 3 parts – 

Descriptive Analytics

It is the study of historical data to find out unknown patterns and trends. It is done using two methods – data aggregation and data mining. It gives a very simple way for a wide set of audiences to understand the intel from past data. Annual reports and sales reports are classic examples of descriptive analytics.

Diagnostic Analytics

It is an advanced mode of study which considers probability and tries to predict the future. Part of data mining also comprises statistical modeling and machine learning techniques to churn out predictive insights with diagnostics analytics from the data set. Credit score prediction and disease diagnosis tools are examples of diagnostic analytics use cases.

Prescriptive Analytics

It is the last and most advanced stage where analysts prescribe solutions or courses of action that should be taken to avoid existing problems or uplift business problems. It tries to find out from the historical pattern of the data which action would be the best from the given choices. It allows the users to select a course of action rather than just data monitoring.

Is Business Analytics the Future?

There is no doubt that the future will be data-led. From mundane activities to complex business ideas, everything will be completely data-driven. Already, businesses that have implemented data science and analytics have shown a competitive advantage over their close competitors.

According to research, 47% of organizations firmly trust that data analytics have significantly transformed their respective industry. 40% of businesses have already made it their top priority to fetch insights from their raw, unstructured data.

When such widespread adoption is happening in all industries, it is imperative to say that the next industrial revolution will happen based on data insights. Only those companies that put business intelligence into use will reap greater benefits than their competitors. Are you on the list?

Final Thoughts

Once you understand and get to know what all data science can do, you will definitely find means and ways to prioritize it on your list of quality enhancements desired.

You cannot ignore the way that is basic to take your business to the next level. The market is no longer what it used to be and therefore you will need assistance as well as guidance from the experts of this field.

In terms of data science – domain expertise, critical thinking, good programming skills along knowledge of statistics and mathematics are required to draw out productive outcomes.

DataToBiz is an AI & data science consultancy firm that helps you draw out insightful queries with your business data and offer solutions that change the way you work.

We provide well-tailored customized data solutions for every business. Contact us to know more about the value addition process to your business through data science.

Data Science & Analytics

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